code: def __init__(self, config, weights, biases):
      super(DSEncoder, self).__init__()
      self.FinalLayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
      self.layer = nn.ModuleList(
          [
              copy.deepcopy(
                  DeepSpeedTransformerLayer(config, weights, biases)
              )
              for _ in range(config.num_hidden_layers)
          ]
      )
      self.grads = []
      self.pre_or_post = config.pre_layer_norm
  
imports: import numpy
  import torch
  import pytest
  import random
  import copy
  from torch import nn
  from unit.modelingpreln import BertEncoder
  from unit.modeling import BertLayerNorm, BertConfig, BertEncoder
  from deepspeed import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
  from deepspeed.accelerator import get_accelerator
  from unit.common import DistributedTest
label_desc: Use image node_id 1 to create a new DSEncoder object from inherited base classes: nn with example: obj = DSEncoder(config, weights, biases)